Team Members: Hengyu Zhang, Junya Jian, Sirui Tang, Daniel Jiao
The University of Rochester’s Accounts Payable Department processes over one million invoices annually, making it a challenge to identify and prevent financial discrepancies. The goal of this project was to develop a system that detects anomalies and duplicates payments efficiently, reducing the department’s reliance on costly external service providers and improving operational accuracy. To achieve this goal, students utilized advanced machine learning models, such as Lightweight Online Detector of Anomalies (LODA), Isolation Forest, and One-Class Support Vector Machine (OCSVM), which were integrated into a stacked ensemble for anomaly detection. The team also developed a robust Exact Matching method to detect duplicate payments caused by formatting inconsistencies or entry errors. Ultimately, the system successfully flagged over 53,000 potential anomalies and duplicates, prioritizing high-risk transactions and streamlining the audit process. The project’s final deliverables included an automated detection framework and a list of actionable insights for the Accounts Payable Department.